Learning of spatiotemporal behaviour in cellular neural networks

In this paper the problem of learning spatiotemporal behaviour with cellular neural networks is analysed and a novel method is proposed to approach the problem. The basis for this method is found in trajectory learning with recurrent neural networks. Despite of similarities, the two learning problems have underling differences which make non‐trivial a direct mapping into the problem at hand. In order to solve the problem, a new cost function is proposed, which also assimilates time instants as parameters to be optimized. As a consequence, it does not force the desired spatiotemporal behaviour to be learned in its original speed, and thus different speed versions of the desired behaviour are allowed to be learned; hence, also providing a promising direction for increasing the speed of existing applications. Learning examples are presented for different classes of spatiotemporal dynamics including spiral autowaves. Results of simulation and on‐chip learning show that the proposed approach is able to learn these dynamics with cellular neural networks. Copyright © 2006 John Wiley & Sons, Ltd.